For Corporate & College Trainings Call us Today ! +91 8076075781 |
  • No products in the cart.

45,000 40,000

Program Highlights

50 Hours of
Instructor-led Online Training

3 Months
Course Duration

Mentoring Sessions

Extra Classes for
Non-Technical Candidates

10+ Hands-On

Github Portfolio &
Resume Building

1 Month of
Assured Internship

100% Placement

Our Alumni

Katyayini Cherukumudi

Recently completed Data Science course; Trainer Nitin is really good in the way he taught with real-time examples. The way he provided training is completely different from other institutes. I strongly recommend joining this course to whoever is looking to learn Data Science as it is really worth it.


B Layek

I really enjoyed the data science classes with Nitin. The good thing is he made me understand the essence of data science in the present day through his training classes and how we can use all the tools to get the best out of the data available. The best thing

Tata Consulting Services (US)

Ajanth Raghuram

Thank you Xilytica for providing such great training in Data Science & Machine Learning with Python. Must be recommended to all the people who are looking to build a future in Data Science. Great job in covering all the tasks and topics in an easy-to-follow rhythm.


First of all, I have to thank Xilytica and my Instructor Nitin for enlightening me about the concepts of Data Science. Here at Xilytica, the Data Science journey lead by Nitin was an awesome one. As a mainframe developer for more than 8+ years, I did not get enough chance

Data Science Independent Consultant (US)


I joined Xilytica after doing a lot of research as I was looking for an institute where I can learn AI/ML not only a job or knowledge but also where I can get a direction on how to start a start-up in the field of analytics and machine learnings. I


Abhay Zaqui

The classes were well-organized and quite informative as we were always being taught with real datasets & real-life examples. What helped even more in a better understanding of concepts were the case studies and the project, in which we had to apply all the theory. Helped us solve real-time problems.


Geethanjali Twarakavi

I am very pleased with the Data Science training. When I started I had no knowledge of reporting and now I feel very confident in the area. The exercises given were very helpful and it was ensured that the subject and concepts were well understood. I highly recommend Xilytica for


Nagendra Hunsur

Xilytica is a great place for developing a foundation in Analytics. The faculty has super knowledge & a good grasp of Analytics and the study material, case studies, assignments & projects are easy to understand & to practice. Students are provided timely guidance and classes at convenient times. It was

Shridhara K

I had a wonderful experience while learning Data Science from Xilytica. Our trainer Nitin took the time to clear all the concepts clearly. I find the training really informative and descriptive and to the point. I liked the Instructor-led Online training approach as it helps to completely cover topics without

Our Alumni Work at

Data Science @ Xilytica

  • Scope Of Data Analyst
  • Course Objective
  • Course Is For
  • Course Prerequisites

Data Science is one of the fastest evolving fields & a Data Scientist’s job is one of the fastest growing and highest paid in tech.  As there is a cut-throat competition in the market, top organizations are turning their minds to data analytics to identify new market opportunities to design their services and products. Surveys show that 75% of top organizations consider data analytics an essential component of business performance. This is where data scientists come in. Data scientists know how to use their skills in math, statistics, programming, and other related subjects to organize large data sets. Then, they apply their knowledge to uncover solutions hidden in the data to take on business challenges and goals. They are thus able to contribute to their organisation’s business goals. So, learning data science through effective training can give you a bright future. Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! It is a rewarding career that allows you to solve some of the world’s most interesting problems!  As per, a Data Scientist (IT) with Big Data Analytics skills earns an average salary of Rs 706,750 per year in India.

Learning path in this Data Analytics, Data Science & Machine Learning program is specifically designed to give you the tools that you need to be successful in this field. Our innovative approach to this training by combining deep knowledge and collaborative learning environment will help you to develop real skills in analytics. By the end of this training, you will:

  • Can expect to gain the knowledge of analytical techniques required by organizations to take strategic decisions, and by solving our case studies you will know how analytical techniques can be used to get impactful insights from real data
  • You will be able to forecast sales, Identify customer segments, identify drivers of sales/profit, perform regression, analyze customer comments and do much more after the course
  • Understand essential statistical concepts including measures of central tendency, dispersion and  correlation, Inferential Statistics & Hypothesis Testing
  • Understand  Databases & Master SQL concepts & Queries
  • Learn & implement Python concepts.
  • Learn how to interpret data in Python using multi-dimensional arrays in NumPy, manipulate DataFrames in pandas
  • Perform data analytics & gain insights on several data visualisation using popular Python libraries, such as Numpy, Pandas, Matplotlib, Plotly, Seaborn, & many more
  • Build & Optimise Machine Learning models such Linear Regression, Logistic Regression, Support Vector Machine, K Nearest Neighbour, Decision Tree, Random Forest, K means Clustering, Hierarchical Clustering, Principal Component Analysis etc.
  • Present information in the form of metrics, KPIs, reports, and dashboards
  • Learn and create an impressive dashboard using Tableau, showcasing business insights in the form of different chart types.
  • IT professionals looking for career or technology change to Data  Analytics & Data Science
  • BPO industry or Non Technical professionals who are looking for career in Data  Analytics & Data Science 
  • Engineering graduates who want to build a career in Data Science & Analytics
  • Non- technical & MBA graduates who are interested in building a career in Data Science
  • Anyone who likes manipulating data & loves getting insights out of data
  • No prior programming experience is required. We will start from the very basics & gradually take to advanced level
  • You’ll need to install Anaconda, MySQL, Workbench, Tableau Public. We will show you how to do that step by step

Curriculum For Certification In
Data Analytics & Business Intelligence

Introduction To Data Science & Machine Learning

  • What is Data Science & Machine Learning
  • Importance of Data Science & Machine Learning
  • Demand for Data Science Professional
  • The lifecycle of the Data Science & Machine learning Project
  • Tools and Technologies used in Data Science

Introduction To Python Programming

  •  Introduction To The Course
  •  Environment Set Up
  •  Overview Of Anaconda, Jupyter notebook &  Spyder
  •  Installation Set-up
  •  Python Objects & Data Structure Basics – Numbers, Strings, Boolean
  •  Introduction To List
  •  Introduction To Dictionaries
  •  Introduction To Tuples
  •  Introduction To Sets
  •  Comparison Operators
  •  If, Elif & Else Statements
  •  For Loop
  •  While Loop
  •  Functions & Methods
  •  Data Analysis Using Numpy
  • Data Analysis Using Pandas

Mathematics & Linear Algebra

  • Vectors & Matrices
  • Matrix Operations
  • Eigenvectors & Eigenvalues


Introduction To Descriptive Statistics 

  • Types of Data
  • Basic Plots Types
  • Mean, Mode & Median
  • Skewness
  • Variance
  • Standard Deviation
  • Covariance
  • Correlation
  • Histogram
  • Boxplot
  • Scatter Plot
  • Bar Chart


Introduction To Inferential Statistics

  • What is Inferential Statistics
  • Sampling techniques
  • Sample Vs Population
  • Random Variable
  • Probability Distribution
  • Standard Normal Distribution
  • Central Limit Theorem
  • Standard Error
  • Estimators
  • Confidence Interval
  • Z-Score
  • Student’s T Distribution
  • T-Score
  • Margin Of Error


Introduction To Hypothesis Testing

  • Basics Of Hypothesis Testing
  • Null Vs Alternate Hypothesis
  • Significance Error
  • False Positive, False Negative
  • Type 1 Error & Type 2 Error
  • P-Value
  • Sample & Population Tests
  • ANOVA - Analysis Of Variance

Exploratory Data Analysis 

  • Introduction to EDA
  • Data Sourcing & Data cleaning
  • Data Manipulation
  • Dealing With Missing Values
  • Outliers treatment
  • Types of variables
  • Univariate Analysis on Unordered, ordered and quantitative variables
  • Rank-Frequency and Power Law distribution
  • Bivariate Analysis
  • Correlation


Data Visualisation

  • Data Visualization Using Matplotlib & Seaborn
  • Introduction to Matplotlib & Seaborn
  • Basic plotting
  • Figures and sub plotting
  • Box plot, Histograms, Scatter plots, image loading


Introduction To Machine Learning With Python

  • Types Of Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Regression Models
  • Classification Model


Data Preprocessing

  • Feature Scaling Data
  • Splitting Data Into Training & Validation & Test Sets


Supervised Learning


Linear Regression

  • Introduction to Regression Models
  • Correlation Between Two Variables
  • Visualizing Correlation Using Corrgram And Corrplot
  • simple linear regression
  • Multiple Linear Regression
  • Training & Evaluating Regression Models Performance
  • The cost function, R-Square, RMSE & best-fit line
  • Interpretation Of Regression Plots - Residual Vs Fitted Values
  • Q-q Plot, Scale Location Plot, Residuals Vs Leverage Plot
  • Multi-collinearity
  • Adjusted R-Square, P-Value, and VIF
  • Understanding Heteroscedasticity
  • Overfitting


Classification Models

  • Introduction To Classification Model
  • Training And Evaluating Classification Models
  • Confusion Matrix - Accuracy, Precision Etc
  • Interpreting Roc Curve
  • Fine Tuning Models Using Hyper Parameters


Logistic Regression

  • Introduction To Logistic Regression
  • Sigmoid function & Log of odds
  • Threshold Value
  • Building the Logistic Regression Model
  • Visualizing Logistic Regression
  • Interpreting Logistic Regression
  • Making Problistic Predictions
  • Application of logistic regression to multi-class classification
  • Advantages and Disadvantages of Logistic Regression
  • Project On Logistic Regression


Decision Tree

  • Types Of Tree-Based Models
  • Introduction To Decision Tree
  • Understanding CART Model Classification Rules
  • Information Gain, Entropy Gain, Gini Index
  • Building Decision Tree Model
  • Visualizing Decision Tree
  • Interpreting Decision Tree
  • Making Predictions
  • Decision Tree Case Study
  • Project On Decision Tree


Unsupervised Learning 


K - Means Clustering

  • Understanding K – Means Clustering
  • Selecting Right Number Of Clusters
  • Elbow Plot
  • K Means Clustering Case Study
  • Project On K Means Clustering


Portfolio Building

  • Introduction to Git & Github
  • Resume Building
  • Portfolio Building With 20 Plus Projects & Case Studies

  • Introduction To Relational Database
  • Database Fundamentals
  • Creating Table
  • Add, Rename & Drop Columns
  • Insert Data To Table
  • Drop-Table
  • Database Constraints
  • Select Statement
  • Select Distinct
  • Filtering Rows
  • Where Clause
  • OR, Between, IN
  • Aggregate Functions
  • Group By
  • Having Clause
  • Order By
  • Inner Join
  • Full Outer Join
  • Left Join
  • Right Join
  • Union
  • Inbuilt Functions
  • Conditional Expressions


  • Introduction To Tableau
  • Importance Of Visualisation
  • Art Of Storytelling With Charts


Installing Tableau

  • Installing Tableau For Windows
  • Installing Tableau For IOS


Tableau Architecture

  • Tableau Architecture Overview
  • Tableau Interfaces
  • Tableau Distribution & Publishing


Connecting To Data

  • Getting Started With Data
  • Managing Metadata
  • Connecting To Data From Web
  • Getting Data From Text Files
  • Getting Data From Excel Files
  • Connecting To SQL Database


Tableau Basics:

  • Data Extraction
  • Data Blending
  • Navigating Tableau
  • Creating Calculated Fields
  • Adding Colors
  • Adding Labels And Formatting
  • Exporting Your Worksheet


Visualisation & Chart Types

  • Bar Chart
  • Trend Line Chart
  • Area Chart
  • Text Table Chart
  • Box Plot Chart
  • Dual Axis Map
  • Scatter Plot
  • Clustering
  • TreeMap
  • Pie Chart
  • Bubble Chart
  • Bullet Chart
  • Waterfall Chart
  • Word Cloud


Forecasting, Aggregation & Filters

  • Forecasting Using Tableau
  • Types Of Forecasting
  • Working With Data Extracts In Tableau
  • Understanding Aggregation, Granularity, And Level Of Detail
  • Creating An Area Chart & Learning About Highlighting
  • Adding A Filter And Quick Filter


Maps, Scatter Plots & First Dashboard

  • Joining Data In Tableau
  • Creating A Map, Working With Hierarchies
  • Creating A Scatter Plot, Applying Filters To Multiple Worksheets
  • Creating Dashboard
  • Adding An Interactive Action – Filter
  • Adding An Interactive Action – Highlighting


Table Calculations, Advanced Dashboards,Storytelling

  • Section Intro
  • Downloading The Dataset And Connecting To Tableau
  • Mapping: How To Set Geographical Roles
  • Creating Table Calculations For Gender
  • Creating Bins And Distributions For Age
  • Leveraging The Power Of Parameters
  • How To Create A TreeMap Chart
  • Creating A Customer Segmentation Dashboard
  • Advanced Dashboard Interactivity
  • Analyzing The Customer Segmentation Dashboard
  • Creating A Storyline

1. Introduction To Machine Learning

2. Supervised Learning & Unsupervised Learning

3. Regression Metrics

4. Classification Metrics

5. Machine Learning Toolbox

6. Data Preprocessing

7. Linear Regression

8. Advanced Linear Regression

9. Logistic Regression

10. K Nearest Neighbour

11. Decision Tree

12. Random Forests

13. Support Vector Machines

14. K - Means Clustering

15. Hierarchical Clustering

16. Dimensionality Reduction - PCA

17. Machine Learning WebApps

18. Machine Learning Models Deployment

Nitin has over 15 years of rich experience across IT & Analytics technology stack. He has worked for companies like Xerox, Wipro Technologies & have worked across different geographies like India, US & Australia, cutting across different domains like banking, supply chain, health care, e-commerce etc. Nitin has also trained 1000 plus candidates in the field of Data Science & Machine Learning. He likes exploring and solving challenging and competitive industrial problems in the field of Analytics, NLP, & Deep Learning, and converting them into actionable insights. Passionate about building scalable, high-quality practical data-driven solutions and likes to talk about futuristic ideas and product engineering. He has led several data science teams while pairing with machine learning researchers and engineers to maximise the speed of experimentation and production. His interests include Machine Learning, Image Processing, Computer Vision, Deep Learning and he has a strong understanding of analytics in the retail, marketing, and finance areas. 

Our Data Science Experts are always available to take your query​!

+91 72590 78678

    • Program Features
    • Career Support
    • Data Analyst Job Stats

    Program Features

    Join any batch based on your availability

    Extra classes for Non-Technical candidates

    One-on-One Mentoring & Career Guidance

    Job-specific Interview Preparation

    Career Support

    Portfolio Building

    Building Resume

    Interview Preparation

    Assured Internship

    Personalized mentorship

    Support After Training

    Data Science Job Stats

    As Per IBM report, Data & Analytics Jjobs are predicted to touch 2.5 million by 2020

    As Per Glassdoor, Entry Level Data Analyst's Salary In India is Rs 5.0 Lacs/ Year​

    As per Glassdoor, Data Analyst's Salary in US is $67,900/ Year

    Analytics Insight predicts 3 Million Job opening in Data Science in 2021 worldwide

    Fundamental & Industry Projects

    Tools We Use

    Slide 2.0 Slide Slide Slide Slide